29 мар. 2024 г. · In this blog post, we'll walk through the process of building a text classification model using the DistilBERT model.

Text classification is a common NLP task that assigns a label or class to text. Some of the largest companies run text classification in production.

DistilBERT is a small, fast, cheap and light Transformer model trained by distilling BERT base. It has 40% less parameters than google-bert/bert-base-uncased. DistilBertConfig · DistilBertForQuestionAnswering

Explore and run machine learning code with Kaggle Notebooks | Using data from No attached data sources.

18 февр. 2021 г. · DistilBERT is the first in the list for text classification task (a fine-tune checkpoint of DistilBERT-base-uncased, fine-tuned on SST-2).

28 мая 2024 г. · Some potential use cases include: Text classification**: Classify documents, emails, or social media posts into categories like sentiment, topic ...

DistilBERT is a smaller and faster version of the BERT model. It uses a transformer architecture and accepts input in the form of tokenized text sequences.

Sequence Classification with IMDb Reviews. We will download, tokenize, and train a model on the IMDb reviews dataset.

8 февр. 2022 г. · The model 'DistilBertModel' is not supported for text-classification. Supported models are ['FNetForSequenceClassification', 'GPTJForSequenceClassification', ' ...

This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: Train Loss: 0.2741 ...